# Unsupervised speech representation learning using WaveNet autoencoders

arXiv: https://arxiv.org/abs/1901.08810 · HTML: https://mlsanity.com/paper/1901.08810/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2019.

## Built on by (most cited first, 30 of 90)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2109.13226/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting](https://mlsanity.com/paper/2210.10542/) (2022)
- [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://mlsanity.com/paper/2101.07597/) (2021)
- [SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization](https://mlsanity.com/paper/2205.07547/) (2022)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning](https://mlsanity.com/paper/2305.10005/) (2023)
- [Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation](https://mlsanity.com/paper/2109.10595/) (2021)
- [TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation](https://mlsanity.com/paper/2205.12523/) (2022)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation](https://mlsanity.com/paper/2106.02170/) (2021)
- [ASR2K: Speech Recognition for Around 2000 Languages without Audio](https://mlsanity.com/paper/2209.02842/) (2022)
- [Wav2vec-C: A Self-supervised Model for Speech Representation Learning](https://mlsanity.com/paper/2103.08393/) (2021)
- [Variable-rate discrete representation learning](https://mlsanity.com/paper/2103.06089/) (2021)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Human-Centered Concept Explanations for Neural Networks](https://mlsanity.com/paper/2202.12451/) (2022)
- [CLUTR: Curriculum Learning via Unsupervised Task Representation Learning](https://mlsanity.com/paper/2210.10243/) (2022)
- [Decoupling Local and Global Representations of Time Series](https://mlsanity.com/paper/2202.02262/) (2022)
- [Aligned Contrastive Predictive Coding](https://mlsanity.com/paper/2104.11946/) (2021)
- [M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2207.00952/) (2022)
- [NWT: Towards natural audio-to-video generation with representation learning](https://mlsanity.com/paper/2106.04283/) (2021)
- [Concept Graph Neural Networks for Surgical Video Understanding](https://mlsanity.com/paper/2202.13402/) (2022)
- [Efficient Domain Adaptation for Speech Foundation Models](https://mlsanity.com/paper/2302.01496/) (2023)
- [XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition](https://mlsanity.com/paper/2103.08207/) (2021)
- [End2End Acoustic to Semantic Transduction](https://mlsanity.com/paper/2102.01013/) (2021)
- [PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification](https://mlsanity.com/paper/2104.01271/) (2021)

## Abstract (arXiv)

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.